Predicting drug response from single-cell expression profiles of tumours

Pellecchia, Simona; Viscido, Gaetano; Gambardella, Gennaro

doi:10.1101/2023.06.01.543212

Cited by 2 publications

(2 citation statements)

References 71 publications

(126 reference statements)

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“…To this end, we assembled 41,189 single-cell transcriptional profiles encompassing 16 treatment-naive primary TNBC patients (71, 72) (Figure 5F). Since experimental data on afatinib sensitivity for these patients is unavailable, we employed an ensemble prediction approach using two state-of-the-art bioinformatics tools, DREEP (73) and Beyondcell (74), to estimate afatinib sensitivity from single-cell expression profiles (Figure 5G) (Methods). This strategy enabled us to estimate the proportion of afatinib-sensitive cells within each patient’s tumor cell population, which served as a reference for evaluating the performance of scASTRAL.…”

Section: Resultsmentioning

confidence: 99%

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

Pellecchia

Franchini

Viscido

et al. 2023

Preprint

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Although many primary triple negative breast cancers (TNBCs) have enhanced expression of epidermal growth factor receptor (EGFR), EGFR-targeted therapies have shown only variable and unpredictable clinical responses in TNBCs. Here we integrate cellular barcoding and single-cell transcriptomics methods to comprehensively characterize the subclonal dynamics of adaptation of TNBC cells in response to afatinib, tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Integrated lineage tracing analysis on these cells uncovered a pre-existing subpopulation of cells composed of 192 clones (out of the initial 2,336) with distinct biological features, such as elevated IGFBP2 (Insulin-Like Growth Factor Binding Protein 2) expression levels. We demonstrate that IGFBP2 overexpression is sufficient to make TNBC cells tolerant to afatinib treatment by activating the compensatory IGF1-R signalling pathway. Finally, by using deep learning techniques, we devise an algorithm to predict the afatinib sensitivity of TNBC cells from the transcriptional status of identified marker genes of the afatinib response. Our findings provide a new understanding of EGFR-dependent hierarchy in TNBC.

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Section: Resultsmentioning

confidence: 99%

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

Pellecchia

Franchini

Viscido

et al. 2023

Preprint

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“…In this context, the potential of bulk cell lines pre-training followed by transfer learning on SC-level analysis emerges as a compelling approach to overcome these obstacles. A few recent studies, such as DREEP [11], scDEAL [12], and SCAD [13] attempt to leverage publicly available drug-cell line sensitivity profiles to predict drug response at the cellular level. However, previous studies have only used transcriptomic information to pre-train a single drug-specific model, which lack pharmacogenomic information for other drugs and hinder model generalization.…”

Section: Introductionmentioning

confidence: 99%

MetaSCDrug: Meta-Transfer Learning for Single-Cell-Level Drug Response Prediction from Transcriptome and Molecular Representations

Ge,

Sun,

Ren

et al. 2024

Preprint

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Analyzing the drug response at the cellular level is crucial for identifying biomarkers and understanding the mechanisms of resistance. Although studies on the drug response of individual cells can provide novel insights into tumor heterogeneity, pharmacogenomic data related to single-cell (SC) RNA sequencing is often limited. Transfer learning provides a promising approach to translate the knowledge of drug response from bulk cell lines to SC analysis, potentially providing an effective solution to this challenge. Previous studies often use data from single drug-cell lines to pre-train specific models and adapt the models on SC datasets, which lack pharmacogenomic information from other drugs and hinder model generalization. In this work, we introduce MetaSCDrug as a unified meta pre-training framework that integrates molecular information with transcriptomic data to simultaneously modeling cellular heterogeneity in response to multiple pre-trained drugs and generalize to unseen drugs. Our model requires only one pre-training session, followed by fine-tuning on multiple single-cell datasets by few-shot learning, achieving an average of 4.58% accuracy increase in drug response prediction compared to the baselines. Furthermore, our meta pre-training strategy effectively captures transcriptome heterogeneity in the generalization of unseen drugs, achieving a 20% improvement over the model without meta pre-training. Case studies of our framework highlight its capability to identify critical genes for resistance, providing a method for exploring drug action pathways and understanding resistance mechanisms.

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Predicting drug response from single-cell expression profiles of tumours

Cited by 2 publications

References 71 publications

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

MetaSCDrug: Meta-Transfer Learning for Single-Cell-Level Drug Response Prediction from Transcriptome and Molecular Representations

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